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7th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2025

作者： Oh, Samgwon Oh, Seungmin Lee, Junghoon Kim, Doyeon Hahn, Minsoo Kim, Jinsul Chonnam National University Department of Intelligence Electronics Computer Engineering Gwangju Korea Republic of Chonnam National University Department of Intelligence Information Convergence Gwangju Korea Republic of Astana IT University Department of Computational and Data Science Astana Kazakhstan

ISBN: (纸本)9798331507022

The effective generation of synthetic time-series data is crucial for addressing data scarcity in various domains such as network monitoring, financial analysis, and healthcare. This study explores image-based approaches for transforming time-series data into visual representations, enabling the application of advanced image synthesis techniques. We systematically evaluate multiple transformation methods, including recurrence plots, Gramian Angular Fields (GAF), and Markov transition fields, to identify the most suitable techniques for preserving temporal and structural characteristics. The generated synthetic data are validated using statistical similarity metrics and tested on machine learning tasks, such as anomaly detection and forecasting, to assess their practical utility. Experimental results demonstrate that image-based synthetic data generation not only retains essential time-series features but also enhances model robustness under limited data conditions. This research contributes to the growing field of synthetic data generation by providing insights into the integration of time-series and image processing techniques. Future work will focus on refining these methods and exploring their scalability across diverse applications. © 2025 IEEE.

关键词： Photointerpretation

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Customized Billboard Advertisement through Age and Gender Identification

Customized Billboard Advertisement through Age and Gender Id...

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Distributed Systems, computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Shanmugasundaram Hariharan Sreenivasulu Gogula Vinay Kukreja R. D. S Terese Sheeba V. Perumal Jose P Department of Artificial Intelligence and Data Science Vardhaman College of Engineering Hyderabad India Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Centre for Research Impact & Outcome Chitkara University Rajpura India Department of Electronics and Communication Engineering DMI Engineering College Chennai India Dept. of Electrical and Electronics Engineering Vardhaman College of Engineering Hyderabad India Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This work provides a novel and sustainable advertisement display system that uses trained models, such as CNN and SSD, to adapt to the age and gender preferences of bystanders. By integrating a webcam with the advertising screen, the system is able to identify the age and gender of passersby based on the faces that are detected and processed by trained models. Customers can personalize adverts to certain groups by accessing the classification results via a website, which is accessed and stored in real-time on a database server. The system has a motion sensor for energy economy and runs continuously, updating every two seconds. With 91.7% and 95.5% accuracy rates for age and gender classification, respectively, the technology guarantees accurate content delivery. It also has the capacity to show news and guidance in crowded places, thereby fostering societal awareness and knowledge. The work well analyzes the gender detection using conventional methods and reveled it significance.

关键词： Deep learning Adaptation models Accuracy Webcams Biological system modeling Display systems Real-time systems Motion detection Servers Faces

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An Object Driven Fuzzy Decision Model for Sugarcane Yield Prediction in Indonesia's Sugar Industry 7

An Object Driven Fuzzy Decision Model for Sugarcane Yield Pr...

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7th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2024

作者： Utama, Ditdit Nugeraha Asrol, Muhammad Suharjito Harito, Christian Napthaleni Syafrudin, Muhammad Bina Nusantara University BINUS Graduate Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program Industrial Engineering Department Jakarta11480 Indonesia Sejong University Department of Artificial Intelligence and Data Science Seoul05006 Korea Republic of

ISBN: (纸本)9798331519643

A sugarcane yield of one plantation area depends on several independent variables. Practically it is challenging to predict accurately by using conventional methods. This study aims to develop a decision model based on a combination of fuzzy logic and object-oriented methods to predict sugarcane yield. The research is conducted in four main stages, employing object-oriented methods for model design and fuzzy logic for model construction. Object and activity diagrams are used for the object-oriented model design. The fuzzy membership functions employed are a combination of trapezoidal and triangular shapes. The resulting decision model can simulate 2,225 data from plantation areas in Indonesia. Based on the 10 examples of plantation area data in Indonesia, plantation number one obtained the largest sugarcane yield, which was 4.79%, with a similarity value of 0.90 (when compared to manual calculations as its ground truth). This similarity value is a higher value when compared to the average similarity value, which is 0.89. © 2024 IEEE.

关键词： Sugar industry

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Swarm Intelligence for Multi-Robot Coordination in Agricultural Automation

Swarm Intelligence for Multi-Robot Coordination in Agricultu...

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International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： L.B. Abhang Annapurna Gummadi Ravindra Changala Veera Ankalu Vuyyuru Sabareesh R I Infant Raj Automation and Robotics Dept Pravara Rural Engineering CollegeLoni under. SPPU University Pune India Department of CSE (Data Science) CVR College of Engineering Hyderabad India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Ecsotics Farm Foods Private Limited Bangalore India Department of Computer Science and Engineering K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

The research investigates the optimization of multi-robot coordination in agricultural automation through Particle Swarm Optimization. The methodology unfolds in four key aspects: firstly, by harnessing swarm intelligence principles, the approach enhances coordination among multiple robots in agricultural settings, fostering collaboration and dynamic task allocation in response to real-time changes. Secondly, the integration of Particle Swarm Optimization (PSO) introduces a powerful optimization technique, enabling the algorithm to dynamically adapt the swarm's configuration. This adaptability optimizes task allocation and individual robot positions, enhancing overall efficiency. Thirdly, the methodology systematically tackles scalability, adaptability, and robustness challenges in agricultural automation. By synergizing swarm intelligence and PSO, it ensures efficient scalability with an increasing number of robots and responsiveness to evolving agricultural demands. The holistic design makes it well-suited for practical implementation in real-world agricultural scenarios. A series of experiments varying PSO parameters were conducted, revealing nuanced relationships between iterations, swarm size, and coefficients and their impact on convergence time and fitness metrics. The outcomes demonstrate the trade-offs involved in selecting these parameters for efficient coordination in agricultural tasks. A comparative analysis with ABO and ACO highlights PSO's superior performance, achieving remarkable fitness value of 98% in the context of robotic swarm applications.

关键词： Measurement Automation Robot kinematics Scalability Heuristic algorithms Robustness Real-time systems Resource management Particle swarm optimization Optimization

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Class-Aware Contextual Information for Semantic Segmentation

Class-Aware Contextual Information for Semantic Segmentation

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Huadong Tang Youpeng Zhao Yingying Jiang Zhuoxin Gan Qiang Wu School of Electrical and Data Engineering University of Technology Sydney Samsung Research China-Beijing(SRC-B) Department of Computer Science University of Central Florida

Exploring spatial contextual information is a well-adopted approach to achieving better semantic segmentation performance. However, most existing methods neglect the class association between the neighboring pixels. In this paper, we propose a CACINet, which consists of a Semantic Affinity Module (SAM) and a Class Association Module (CAM), to generate class-aware contextual information among pixels on a fine-grained level. SAM analyzes the affiliation of any two given pixels belonging to the same or different class. It produces intra-class and inter-class pixel contextual information. CAM classifies the image into different class regions globally and then it encodes the pixel based on the degree of affiliation of the pixels with each class in the image. In this way, it augments the class affiliation of the pixels into the corresponding context calculation. Comprehensive experiments demonstrate that the proposed method achieves competitive performance on two semantic segmentation benchmarks: ADE20K and PASCAL-Context.

关键词： Semantic segmentation Semantics Benchmark testing Acoustics Speech processing Dispersion

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A Comprehensive review on data Mining Techniques in managing the Medical data cloud and its security constraints with the maintained of the communication networks

A Comprehensive review on Data Mining Techniques in managing...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： A. Uthiramoorthy Ankit Bhardwaj Jitendra Singh Kumud Pant Mohit Tiwari José Luis Arias Gonzáles Department of Computer Science Rathinam College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Science and Engineering Data Engineering Leader The Math Company University of Connecticut University of Connecticut Law College Dehradun Uttaranchal University Department of Biotechnology Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Biotechnology Graphic Era Hill University Dehradun Uttarakhand India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Paschim Vihar Delhi Business Department Pontifical Catholic University of Peru

Over the last 10 years, data mining has become more important, and the field of healthcare research has seen a significant uptick in activity. Most of the applications that were presented may be grouped into two distinct classifications: decision support benches and policy formulation. It is still difficult to come across books in the field of healthcare that are worth reading. This review article provides an overview of the current health sector study using a variety of DM approaches and algorithms to look at various diseases such as cancer, diabetes, HIV, and skin co-related disorders and their accurate assessment. Additionally, a startling discovery that was provided to define the article is also provided.

关键词： Industries Ethics Law Medical services Predictive models Skin data mining

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Parameter-Efficient Vision Transformer with Linear Attention

Parameter-Efficient Vision Transformer with Linear Attention

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IEEE International Conference on Image Processing

作者： Youpeng Zhao Huadong Tang Yingying Jiang Yong A Qiang Wu Jun Wang Department of Computer Science University of Central Florida School of Electrical and Data Engineering University of Technology Sydney Samsung Research China-Beijing (SRC-B)

Recent advances in vision transformers (ViTs) have achieved outstanding performance in visual recognition tasks, including image classification and detection. ViTs can learn global representations with their self-attention mechanism, but they are usually heavy-weight and unsuitable for resource-constrained devices. In this paper, we propose a novel linear feature attention (LFA) module to reduce computation costs for vision transformers and combine efficient mobile CNN modules to form a parameter-efficient and high-performance CNN-ViT hybrid model, called LightFormer, which can serve as a general-purpose backbone to learn both global and local representation. Comprehensive experiments demonstrate that LightFormer achieves competitive performance across different visual recognition tasks. On the ImageNet-1K dataset, LightFormer achieves top-1 accuracy of 78.5% with 5.5 million parameters. Our model also performs well when transferred to object detection and semantic segmentation tasks. On the MS COCO dataset, LightFormer attains mAP of 33.2 within the YOLOv3 framework, and on the Cityscapes dataset, with only a simple all-MLP decoder, LightFormer achieves mIoU of 78.5 and FPS of 15.3, surpassing state-of-the-art lightweight segmentation networks.

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Inferring Causality from Time Series data based on Structural Causal Model and its application to Neural Connectomics

arXiv

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arXiv 2023年

作者： Biswas, Rahul Sripada, SuryaNarayana Mukherjee, Somabha Department of Electrical and Computer Engineering University of Washington Seattle98195 United States Center for Research on Science and Consciousness Redmond98052 United States Department of Statistics and Data Science National University of Singapore 117546 Singapore

Inferring causation from time series data is of scientific interest in different disciplines, particularly in neural connectomics. While different approaches exist in the literature with parametric modeling assumptions, we focus on a non-parametric model for time series satisfying a Markovian structural causal model with stationary distribution and without concurrent effects. We show that the model structure can be used to its advantage to obtain an elegant algorithm for causal inference from time series based on conditional dependence tests, coined Causal Inference in Time Series (CITS) algorithm. We describe Pearson's partial correlation and Hilbert-Schmidt criterion as candidates for such conditional dependence tests that can be used in CITS for the Gaussian and non-Gaussian settings, respectively. We prove the mathematical guarantee of the CITS algorithm in recovering the true causal graph, under standard mixing conditions on the underlying time series. We also conduct a comparative evaluation of performance of CITS with other existing methodologies in simulated datasets. We then describe the utlity of the methodology in neural connectomics - in inferring causal functional connectivity from time series of neural activity, and demonstrate its application to a real neurobiological dataset of electro-physiological recordings from the mouse visual cortex recorded by Neuropixel probes. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

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Lightweight Authenticated Task Offloading in 6G-Cloud Vehicular Twin Networks

Lightweight Authenticated Task Offloading in 6G-Cloud Vehicu...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Al-Shareeda, Sarah Ozguner, Fusun Redmill, Keith Duong, Trung Q. Canberk, Berk The Ohio State University Electrical and Computer Engineering Department United States Bts Group Turkey Istanbul Technical University Department of Ai and Data Engineering Turkey School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom Memorial University of Newfoundland Faculty of Engineering and Applied Science St. John'sNL Canada School of Computing Engineering and The Built Environment Edinburgh Napier University United Kingdom

ISBN: (纸本)9798350368369

Task offloading management in 6G vehicular net-works is crucial for maintaining network efficiency, particularly as vehicles generate substantial data. Integrating secure communication through authentication introduces additional computational and communication overhead, significantly impacting offloading efficiency and latency. This paper presents a unified framework incorporating lightweight Identity-Based Cryptographic (IBC) authentication into task offloading within cloud-based 6G Vehicular Twin Networks (VTNs). Utilizing Proximal Policy Optimization (PPO) in Deep Reinforcement Learning (DRL), our approach optimizes authenticated offloading decisions to minimize latency and enhance resource allocation. Performance evaluation under varying network sizes, task sizes, and data rates reveals that IBC authentication can reduce offloading efficiency by up to 50% due to the added overhead. Besides, increasing network size and task size can further reduce offloading efficiency by up to 91.7%. As a countermeasure, increasing the transmission data rate can improve the offloading performance by as much as 63%, even in the presence of authentication overhead. The code for the simulations and experiments detailed in this paper is available on GitHub for further reference and reproducibility [1]. © 2025 IEEE.

关键词： Authentication Deep Reinforcement Learning Identity-Based Cryptography Proximal Policy Optimization Task Offloading Vehicular Twin Networks

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In-Context Convergence of Transformers

arXiv

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arXiv 2023年

作者： Huang, Yu Cheng, Yuan Liang, Yingbin Department of Statistics and Data Science University of Pennsylvania United States National University of Singapore Singapore Department of Electrical and Computer Engineering The Ohio State University United States

Transformers have recently revolutionized many domains in modern machine learning and one salient discovery is their remarkable in-context learning capability, where models can solve an unseen task by utilizing task-specific prompts without further parameters fine-tuning. This also inspired recent theoretical studies aiming to understand the in-context learning mechanism of transformers, which however focused only on linear transformers. In this work, we take the first step toward studying the learning dynamics of a one-layer transformer with softmax attention trained via gradient descent in order to in-context learn linear function classes. We consider a structured data model, where each token is randomly sampled from a set of feature vectors in either balanced or imbalanced fashion. For data with balanced features, we establish the finite-time convergence guarantee with near-zero prediction error by navigating our analysis over two phases of the training dynamics of the attention map. More notably, for data with imbalanced features, we show that the learning dynamics take a stage-wise convergence process, where the transformer first converges to a near-zero prediction error for the query tokens of dominant features, and then converges later to a near-zero prediction error for the query tokens of under-represented features, respectively via one and four training phases. Our proof features new techniques for analyzing the competing strengths of two types of attention weights, the change of which determines different training phases. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamics

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